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Nanoflow LC-MS for High-Performance Chemical Isotope Labeling Quantitative Metabolomics Zhendong Li, Jaspaul Tatlay, and Liang Li Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.5b03209 • Publication Date (Web): 19 Oct 2015 Downloaded from http://pubs.acs.org on October 26, 2015

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Analytical Chemistry

(Manuscript submitted to Anal. Chem. as an Article on August 20, 2015; Revised on October 16, 2015)

Nanoflow LC-MS for High-Performance Chemical Isotope Labeling Quantitative Metabolomics

Zhendong Li†, Jaspaul Tatlay†, and Liang Li* Department of Chemistry, University of Alberta, Edmonton, Alberta, Canada

†Authors contributed equally. * Corresponding author. E-mail: [email protected].

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2

Abstract Nanoflow liquid chromatography mass spectrometry (nLC-MS) is prevalent in the proteomics field to analyze a small amount of protein and peptide samples. However, this technique is currently not widespread in the metabolomics field. We report a detailed investigation on the development of an nLC-MS system equipped with a trap column for highperformance chemical isotope labeling (CIL) metabolomic profiling with deep coverage and high sensitivity. Experimental conditions were optimized for profiling the amine/phenol submetabolome with

13

C-/12C-dansylation labeling. Comparison of analytical results from nLC-

MS and microbore LC-MS (mLC-MS) was made in the analysis of metabolite standards and labeled human urine and sweat samples. It is shown that, with a 5-µL loop injection, 7 labeled amino acid standards could be detected with S/N ranging from 7 to 150 by nLC-MS with an injection of 5 nM solution containing a total of 25 fmol labeled analyte. For urine metabolome profiling where the sample amount was not limited, nLC-MS detected 13% more metabolites than mLC-MS under optimal conditions (i.e., 4524±37 peak pairs from 26 nmol injection in triplicate vs. 4019±40 peak pairs from 52 nmol injection). This gain was attributed to the increased dynamic range of peak detection in nLC-MS. In the analysis of human sweat where the sample amount could be limited, nLC-MS offered the advantage of providing much higher coverage than mLC-MS. Injecting 5 nmol of dansylated sweat, 3908±62 peak pairs or metabolites were detected by nLC-MS, while only 1064±6 peak pairs were detected by mLCMS. Because labeled metabolites can be captured on a reversed phase (RP) trap column for large volume injection and are well separated by RPLC, the CIL platform can be readily implemented in existing nLC-MS instruments such as those widely used in shotgun proteomics.


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3

Introduction The growth of metabolomics in the past decade has been directly linked to the development of modern analytical techniques that are able to quantitatively profile a wide range of metabolites in a sample. Liquid chromatography mass spectrometry (LC-MS) has become a powerful tool for metabolomic profiling.1, 2 To increase the sensitivity of the LC-MS platform, researchers are continually developing more sensitive mass spectrometers, new LC techniques and improving ionization efficiency of metabolites. The latter can be done using chemical labeling such as isotope encoded chemical derivatization or chemical isotope labeling (CIL).3-10 In CIL, one isotopic form of a reagent is used to target a broad submetabolome (e.g., all amines and phenols when using dansyl chloride,4 or all carboxylic acids using DmPA5). In parallel, a reference sample of very similar composition but distinct from the sample which is most commonly made by pooling all available samples is labeled with another isotopic form of the reagent.11, 12 The derivatized sample and reference are then mixed together and injected into LCMS for analysis. Peak pairs detected from differentially labeled metabolites are used for metabolite quantification and identification. By using a proper labeling reagent,3-5 CIL LC-MS allows concomitant improvement in LC separation and MS detection. Accurate relative and absolute quantification of thousands of metabolites can be obtained from a single experiment.12 Further sensitivity increase in LC-MS is still highly desirable in handling samples of limited amounts, particularly those requiring multiple analyses. For example, in CIL LC-MS, each labeling reagent covers a selected submetabolome. Therefore, multiple labeling of the same sample using different aliquots needs to be carried out in order to increase the coverage of the overall metabolome. If multidimensional separation of a metabolome or submetabolome is used, the amount of metabolites in individual pre-fractionated aliquots for LC-MS analysis may be



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4 very limited,13-15 requiring a sensitive detection technique. In this regard, there exists a high sensitivity platform that is already widely used in proteomics,16 but less common in metabolomics: the nanoflow-LC MS. Only a few studies were reported using nLC-MS for metabolomic analysis.17-22 This can be attributed to several reasons including technical challenges. In untargeted metabolomic profiling, four modes of LC-MS experiments using two different stationary columns (e.g., reversed phase (RP) and hydrophilic interaction (HILIC) columns) with each operated at positive and negative ion MS detection are often performed on a sample to detect both polar and nonpolar metabolites.23-27 In nLC-MS, it is a relatively timeconsuming process to switch different capillary columns and then optimize their performances thereafter. In addition, injecting a large volume of sample to increase sample loading to nLC is a major challenge.18 nLC-MS systems used for shotgun proteome analysis is often equipped with a trap column to capture peptides in several microliters of volume prior to nLC separation. However, high efficiency trapping of all metabolites with wide variations in chemical and physical properties is very difficult in metabolome analysis. CIL metabolomic profiling using a rationally designed labeling reagent can overcome these technical challenges, because chemical labeling such as dansylation increases hydrophobicity of a labeled metabolite to a great extent so that polar or even ionic metabolites can retain on RP columns after labeling.4, 5 Both RP trap column and analytical column can be used. There is no need to switch columns to handle different classes of metabolites. CIL also reduces the impact of larger retention time shifting in nLC than conventional LC, because quantification is not reliant on accurate chromatographic alignment between different samples and each metabolite is quantified with its own isotopic counterpart as a peak pair in a mass spectrum.11,

12

We note that there were reports of using nLC-MS for quantifying a limited


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5 number of metabolites using chemical isotope labeling.9, 21, 22 However, no trapping was used and the labeling reagents used in these reported studies are expected not to alter the metabolite hydrophobicity to such an extent that would allow efficient trapping using an RP column. In this work, we report a workflow based on nLC-MS equipped with a RP trapping column for routine analysis of chemical isotope labeled metabolomic samples with coverage of a few thousands of metabolites and describe its performance, particularly in comparison with microbore LC-MS (mLC-MS) commonly used in metabolomics. Dansylation labeling was used for analyzing metabolite standards and the amine/phenol submetabolome of human urine and sweat to demonstrate the improvement of detection sensitivity and metabolome coverage by using nLC-MS.

Experimental Section nLC-MS. All nLC-MS experiments were performed on a Waters nanoACQUITY UPLC (Milford, MA, USA) connected to a Waters Q-TOF Premier quadrupole time-of-flight (QTOF) mass spectrometer (Milford, MA, USA) equipped with a nano-ESI source. Mass spectrometer settings were: capillary voltage 3.5 kV, sampling cone 30 V, extraction cone 3.0 V, source temperature 110°C, and collision gas 0.45 mL/min. A 5 μm I.D. PicoTip by New Objective (Woburn, MA, USA) was used with the nano-ESI source. Chromatographic separations were performed on an Acclaim PepMap RSLC C18 (75 μm x 150 mm, 2 μm) and Acclaim PepMap 100 trap column (75 μm x 20 mm, 3 μm). A Waters nanoAcquity C18 (75 μm x 200 mm, 1.7 μm) column and nanoAcquity Atlantis trap column (180 μm x 20 mm, 5.0 μm) was also evaluated. Mobile phase A was 0.1 % LC-MS formic acid in LC-MS water and mobile phase B was 0.1 % LC-MS formic acid in LC-MS acetonitrile. The 45 minute gradient conditions were; 0



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6 min (15% B), 0-2.0 min (15% B), 2.0-4.0 min (15-25% B), 4.0-24 min (25-60% B), 24-28 min (60-90% B), and 28-45 min (90% B). A wash and equilibration injection was run between samples; the gradient was: 0-10 min (90% B), 10-25 min (15% B). The flow rate was 350 nL/min and the injection volume was 5 μL (the maximum volume of the sample loop used) in most cases except that of studying the trapping efficiency. LC-MS. All LC-MS experiments were performed on an Agilent 1100 Series Binary LC System (Santa Clara, CA, USA) connected to the same Q-TOF Premier mass spectrometer used in the nLC-MS experiment, with the nESI source swapped out for an ESI source. Mass spectrometer settings were: capillary voltage 3.5 kV, sampling cone 30, extraction cone 3.0, source temperature 110°C, desolvation temperature 220°C, desolvation gas 800 L/hr, and collision gas 0.45 mL/min. Chromatographic separations were performed on a Waters Acquity BEH C18 column (2.1 mm x 100 mm, 1.7 μm) with the same mobile phases as the nano-LC. The 45 minute gradient conditions were; 0 mins (20% B), 0-3.5 min (20-35% B), 3.5-18 min (3565% B), 18-24 min (65-99% B), 24-37 min (99% B), and 37.1-45 min (20% B). The flow rate was 180 μL/min. nLC-MS Trapping Efficiency. A mixture of amino acids at a concentration of 1 mM each was dansylated4 (see Supplemental Note N1). The dansylation efficiencies for these amino acids have been determined previously by comparing the signal intensities of labeled product and any remaining unlabeled metabolite using LC-MS.4

12

C2 and

13

C2-dansyl labeled amino acids

were mixed 1:1 by volume and diluted to 1000, 2000, 4000, 6000, 8000, and 10000 fold using serial dilution. Injection volume was varied for each diluted sample to ensure 120 fmol of dansylated amino acids are loaded onto the column for each injection. Data was de-noised, smoothed, centered and peak areas extracted using Waters QuanLynx software.


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7 Dynamic Range of Peak Pair Detection.

12

C2-dansylated amino acids were diluted by

half and mixed with undiluted 13C2-dansylated amino acids in a 1:1 volume ratio. The theoretical peak ratio of 12C2- to 13C2-labeled amino acid should be 1:2. The sample was then diluted using serial dilution and increasing sample amounts were injected into the nLC-MS and mLC-MS. Ratios were calculated by dividing the

12

C2-labeled amino acid peak area by the

13

C2-labeled

amino acid peak area. Urine and Sweat Analysis. A human urine sample was split into two vials; one was 12

C2-dansyl labeled and the other was 13C2-dansyl labeled. The 12C2-dansyl urine was quantified

to be 48.2 mM using the LC-UV method28 (see Supplemental Note N1). The

12

C2-dansyl urine

and 13C2-dansyl urine were mixed 1:1 by volume then diluted using serial dilution. These diluted samples were injected at increasing concentrations into the nLC-MS and LC-MS. Peak pairs were then extracted from the processed data using IsoMS.11 A human sweat sample was treated the same way as the urine sample. The concentration of the sweat was determined to be 8.4 mM using the LC-UV method. The 12C2-dansyl sweat and 13

C2-dansyl sweat were mixed 1:1 (v/v) for injection into nLC-MS and mLC-MS for analysis.

Results and Discussion Column Selection. We recognize that some users may re-purpose an existing nLC-MS system used for shotgun proteomic analysis to analyze metabolomic samples for metabolomics. Various factors need to be considered to make such a switch including column selection. We initially used a set of Waters trap column and analytical column used for proteomic analysis to analyze the dansyl labeled urine samples. The resulting chromatogram showed wide peak widths of around 0.8 min with tailing (an example is shown in Supplemental Figure S1A), compared to



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8 widths of ~0.06 min for peptides. This problem was found to be caused by the Waters Symmetry C18 trap which uses high purity silica with end capping. Peak broadening was not observed when the sample was directly loaded onto the analytical column which uses a polymeric bonded phase. It is very likely that a significant amount of residual silanol activity existed in the trap column that caused broadening for the basic dansylated metabolites but not the peptides. We then switched the trap and column set to the Thermo Scientific PepMap 100 C18 set which also used high purity silica. However, the PepMap 100 set was found to give adequate peak widths of 0.2 minutes with reduced tailing (see Supplemental Figure S1B). This set was thus selected for the subsequent experiments. This example illustrates the importance of selecting a proper column and trap combination for profiling labeled metabolites. Separation Parameters. Several nLC parameters were optimized to achieve an optimal coverage of the amine/phenol submetabolome profile within the shortest run time. First, analytical flow rates of 500, 350, 150 nL/min were tested. With the three different flow rates, we found no significant differences in the number of peak pairs or metabolites detected. However, while using 500 nL/min gave the shortest run time, it could increase the backpressure significantly, resulting in popping the fused-silica capillaries out of their fittings. At the lowest flow rate of 150 nL/min, the analysis time was increased by 10 min. Thus the flow rate of 350 nL/min was chosen as a compromise for the work. Next we optimized the gradient separation condition. It was found that the majority of the labeled metabolites eluted between 15% and 60% mobile phase B (acetonitrile 0.1% formic acid). Thus, a shallow gradient from 25% to 60% over 20 min was used to improve separation. The solvent composition of the diluent used to prepare the dansylated samples was also optimized. Initially, the samples were diluted using the same solvent composition as that used for


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9 the dansylation labeling reaction, i.e., 1:1 acetonitrile:water (v/v) 0.1% formic acid, to prevent any potential precipitation of highly non-polar dansylated metabolites. This sample plug with a high organic composition greatly reduced the retention of metabolites on the trap column, causing metabolites to be flushed out of the trap column and into the waste. As a result, a significant amount of early eluting peaks were reduced in intensity (see Supplemental Figure S2A). After testing a number of different diluents, a diluent composed of 1:9 acetonitrile:water (v/v) 0.1% formic acid was found to give no sample loss or precipitation for the urine samples studied (see Supplemental Figure S2B). Trapping Optimization and Efficiency. A trapping column is an integral part of nLCMS for injecting a relatively large volume of samples. It is not commonly used in mLC-MS, as injection of several microliters of sample is compatible with the high flow rate. In nLC-MS, prior to separating on the analytical column, the sample is first pushed through a short trap column, usually at a higher flow rate compared to the analytical flow rate. Analytes are retained on the trap while extra diluent and other non-retaining matrix components are flushed into the waste. This serves two functions: the first is to remove salts and other interfering chemicals and the second is to reduce the time it takes for samples to reach the column. As a result, a large volume of sample can be loaded onto the column in a short time. Figure 1 shows the chromatograms of the separation of a mixture of dansylated amino acids using a 5 μL injection loop at a flow rate of 350 nL/min. Without the use of the trap column, there was a dead time of 16.67 min and the first retained analyte eluted in 23.63 min (see Figure 1A). With the trap column, at a trapping flow rate of 7.0 μL/min, the dead time of the sample loop was reduced to 0.71 min leaving only the dead time of the gradient delay which was 7.10 min (see Figure 1B). Overall, there was a



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10 reduction of 16.53 min in run time when the trap was used. Therefore, analyte trapping is essential for reducing the dead time of nLC-MS operating at nanoliter flow rates. In using the trap, the goal is to have metabolites completely retained on the trap while mobile phase is pushed through at the highest flow rate possible to wash out salts and other nonanalytes. The concern is that with higher flow rate there will be more metabolites that are flushed into the waste. Therefore, the trapping flow rate, trapping mobile phase composition and trapping time need to be carefully balanced. Several trapping flow rates ranging from 1 μL/min to 20 μL/min was tested; 20 μL/min was the highest flow rate possible without over pressuring the trap column. By increasing the trap flow rate, the number of peak pairs detected was reduced due to sample loss. Decreasing the flow rate caused a longer dead time and longer overall run time with no significant increase in peak pair number. The optimal flow rate was found to be 7 μL/min which was the highest flow rate without significant sample loss. The trapping mobile phase composition was optimized to be 2% acetonitrile in water. Increasing the organic composition washed away the sample. Finally, the shortest trapping time to wash the entire sample out of the 5 μL sample loop and onto the trap, in addition to washing out salts, was set at 1 min. While trapping can increase the detection sensitivity by allowing for the injection of a large volume of dilute sample, there is a greater chance that the analytes might be washed out with larger loading volume. After optimizing the trapping conditions, we investigated the trapping efficiency by injecting a series of diluted dansylated amino acid mixtures where the injection amount was kept constant at 120 fmol by adjusting the injection volume and concentration. There was no observed trend that indicated sample loss from 1000 to 10000 fold diluted samples when looking at the measured peak area for selected dansylated amino acids as shown in Figure 2. This shows that the nLC-MS trapping condition used was efficient at trapping


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11 low-concentration

and

high-injection-volume

dansylated

samples

without

affecting

chromatographic separation or incurring significant sample loss. It should be noted that we chose the amino acid standards for this trapping efficiency study, because they represent some of the most polar compounds found in urine, serum or other biological samples. Thus the results of dansyl labeled amino acids on a C18 trap represent the extreme cases of otherwise unretained polar metabolites without labeling. Other metabolites in a biological sample will be more hydrophobic and should be retained on the trap after dansylation even more efficiently. Chromatographic Reproducibility. In untargeted metabolomic studies, reproducible retention time is required for data file alignment to generate accurate abundance information across hundreds of samples that are run on different days or even different weeks. Supplemental Table T1 shows the intraday retention time reproducibility of dansylated amino acids measured using the nLC and mLC. The average relative standard deviation (%RSD) of the nLC retention times was 0.48%, which was significantly worse than the %RSD of the mLC at 0.06%. This confirms reports by other groups that nLC retention time is not as stable as mLC.29 The lower retention time reproducibility may be due to the reduced quality in stationary phase packing in preparing the nLC columns and a larger flow rate variation with nLC pumps vs. mLC pumps. Retention time stability has a negative effect on the quantification of unlabeled metabolites between different samples and several peak alignment methods have been reported to reduce the effect.30 However, with CIL, each 12C2 dansylated metabolite in a sample is quantified relative to the 13C2 dansylated metabolite in a control and thus precise alignment is not required for relative quantification. In addition to retention time, the intensity of the metabolites needs to be stable between sample runs for accurate quantification. Supplemental Table T2 shows the intensity %RSDs of



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12 the dansylated amino acids. The average %RSD of nLC intensities was found to be similar to that of mLC (3.6% vs. 3.3%). Thus, the quantitative precision of the two systems is similar. Sensitivity Improvement. Figure 3A shows the plots of peak areas as a function of sample injection amount for nLC- and mLC-MS using dansyl alanine as an example. Signal saturation was observed for nLC-MS when the analyte concentration was over 48 µM, corresponding to 240 pmol with 5 µL injection. In contrast, even at 238 µM, the peak area obtained by mLC-MS was not very high. In fact, it was slightly lower than that obtained using the solution of 0.5 µM in nLC-MS. This result demonstrates a more than 476-fold increase in mass-detection sensitivity at the high concentration region. At the low limit, as Figure 3B shows, mass spectral signals were still detectable at S/N 61 with the injection of the 0.005 µM or 5 nM solution, corresponding to 0.025 pmol or 25 fmol amount. The limit of detection (LOD) for dansyl alanine was 1.2 nM in mLC-MS and 0.25 nM in nLC-MS. This sensitivity enhancement for detecting dansyl labeled metabolites using nLC-MS is consistent with what others observed for nano-ESI of other types of molecules due to improvement in ionization efficiency, reduced ion suppression and more efficient ion acceptance to MS.31, 32 For other labeled amino acids tested (see Figure 4 and Supplemental Figure S3), injections of 5 nM of dansylated glycine, glutamic acid, asparagine, phenylalanine, leucine and tryptophan gave signals with S/N 130, 120, 11, 30, 150 and 7, respectively, and their corresponding LODs are 0.12 nM in nLC-MS (0.52 nM in mLC-MS; same below), 0.13 nM (0.95 nM), 1.4 nM (>20 nM), 0.5 nM (6.0 nM), 0.10 nM (3.3 nM) and 2.1 nM (8.6 nM). These LODs are significantly lower than those reported using nLC-MS without a trap and with other labeling reagents. LODs of isobaric N,N-dimethyl leucine labeled alanine, phenylalanine, leucine and tryptophan were found to be 110 nM, 7 nM, 30 nM and 10 nM, respectively.21 LODs of


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13 isobaric N-hydroxysuccinimide ester labeled alanine, glycine, glutamic acid, phenylalanine, leucine and tryptophan were determined to be 19 nM, 21 nM, 2 nM, 1 nM, 16 nM and 2 nM, respectively.22 Our results illustrate that with a 5-µL loop injection we can now analyze metabolites at

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